CN117057809A - User behavior analysis method, system, electronic equipment and medium - Google Patents
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- 238000004458 analytical method Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 claims abstract description 30
- 238000012502 risk assessment Methods 0.000 claims abstract description 9
- 230000006399 behavior Effects 0.000 claims description 81
- 238000012545 processing Methods 0.000 claims description 18
- 238000004891 communication Methods 0.000 claims description 17
- 239000013598 vector Substances 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 11
- 238000005070 sampling Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 5
- 230000008030 elimination Effects 0.000 claims description 4
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- 230000009286 beneficial effect Effects 0.000 abstract description 6
- 238000013461 design Methods 0.000 description 6
- 230000002093 peripheral effect Effects 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 3
- 238000012163 sequencing technique Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
Abstract
The application belongs to the technical field of computers, and aims to provide a user behavior analysis method, a system, electronic equipment and a medium. In the implementation process, the risk bearing capacity value of the current appointed user and the user group of the current appointed user are obtained by acquiring the user information of the appointed user, obtaining the user deep information according to the user information, and then obtaining the risk bearing capacity value of the current appointed user according to the user deep information; then, sorting the risk bearing capacity values and the risk bearing capacity values of all users in the user group to obtain a sorting result, and obtaining the risk bearing capacity grade of the current appointed user according to the sorting result; and finally, obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user. In the process, the application realizes the risk assessment of investors, is beneficial to security companies to comprehensively master the risks of investors, and has popularization and application values.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to a user behavior analysis method, a system, electronic equipment and a medium.
Background
The securities industry is one of the important components in the financial service field, and mainly provides the issuing, trading and investment services of securities products such as stocks, bonds, funds and the like. In the securities industry, in order to know the risk bearing capacity and potential risk of an investor, securities companies generally acquire investment experiences, trade styles, risk preferences and the like of users in the form of questionnaires so as to provide more accurate investment services for the investor, thereby improving the satisfaction of the investor in using the securities investment services. However, in using the prior art, the inventors found that there are at least the following problems in the prior art: the actual situation of investors often has a large gap from the expectations expressed in questionnaires, so that the security company has insufficient control over the risks of the investors, and the security company has insufficient early warning and coping capacities on the risks of the investors.
Disclosure of Invention
The application aims to solve the technical problems at least to a certain extent, and provides a user behavior analysis method, a system, electronic equipment and a medium.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a method for analyzing user behavior, including:
acquiring user information of a designated user;
obtaining deep user information according to the user information;
obtaining a risk bearing capacity value of a current appointed user and a user group to which the current appointed user belongs according to the deep user information;
the risk bearing capacity values and the risk bearing capacity values of all users in the user group are ranked, and a ranking result is obtained;
according to the sorting result, obtaining the risk bearing capacity grade of the current appointed user;
and obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user.
The embodiment can evaluate the risk of the investor, and is beneficial to realizing the risk early warning of the investor. Specifically, in the implementation process of the embodiment, the user information of the appointed user is obtained, the user deep information is obtained according to the user information, and then the risk bearing capacity value of the current appointed user and the user group to which the current appointed user belongs are obtained according to the user deep information; then, sorting the risk bearing capacity values and the risk bearing capacity values of all users in the user group to obtain a sorting result, and obtaining the risk bearing capacity grade of the current appointed user according to the sorting result; and finally, obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user. In the process, the risk assessment of investors is realized, the security company can comprehensively master the risk of the investors, and the method and the device have popularization and application values.
In one possible design, the user information includes user basic information and user behavior information; the user basic information is extracted from the user registration information; user behavior information is extracted from the target website by using a Google analysis tool.
In one possible design, after obtaining the user information of the specified user, the method further includes:
preprocessing the user information to obtain preprocessed user information;
and carrying out noise elimination processing on the preprocessed user information to obtain denoised user information so as to obtain user deep information according to the denoised user information.
In one possible design, the user deep information includes user category deep information corresponding to user basic information; correspondingly, obtaining deep user information according to the user information comprises the following steps:
classifying the basic information of the user to obtain user class characteristics;
performing hot independent coding on the user category characteristics, and converting the user category characteristics into user category characteristic vectors;
and performing a first convolution operation on the user category feature vector to obtain user category depth information.
In one possible design, the user deep information further includes user behavior deep information corresponding to the user behavior information; correspondingly, obtaining the deep information of the user according to the user information, and further comprises:
classifying the user behavior information to obtain user behavior characteristics;
performing hot independent coding on the user behavior characteristics, and converting the user behavior characteristics into user behavior characteristic vectors;
and performing a second convolution operation on the user behavior feature vector to obtain user behavior depth information.
In one possible design, obtaining the risk bearing capability value of the current appointed user and the user group to which the current appointed user belongs according to the deep information of the user comprises:
obtaining a risk bearing capacity value of a current appointed user according to the deep information of the user;
according to the risk bearing capacity value, extracting all matched users with the difference value between the sampling risk bearing capacity value and the risk bearing capacity value smaller than a preset value from a pre-stored sampling user group;
and combining all the matched users to obtain the user group of the current appointed user.
In one possible design, after obtaining the risk tolerance level of the currently specified user, the method further includes:
and updating the asset configuration information of the current appointed user according to the risk bearing capacity level to obtain and output updated asset configuration information.
In a second aspect, the present application provides a user behavior analysis system for implementing a user behavior analysis method according to any one of the above; the user behavior analysis system includes:
the user information acquisition module is used for acquiring user information of a designated user;
the user deep information generation module is in communication connection with the user information acquisition module and is used for acquiring user deep information according to the user information;
the risk assessment module is in communication connection with the user deep information generation module and is used for obtaining a risk bearing capacity value of the current appointed user and a user group to which the current appointed user belongs according to the user deep information; the risk bearing capacity value is used for sorting the risk bearing capacity values and the risk bearing capacity values of all users in the user group to obtain a sorting result; the risk bearing capacity grade of the current appointed user is obtained according to the sequencing result;
and the risk early warning module is in communication connection with the risk assessment module and is used for obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user.
In a third aspect, the present application provides an electronic device, comprising:
a memory for storing computer program instructions; the method comprises the steps of,
a processor for executing computer program instructions to perform the operations of the user behavior analysis method of any one of the above.
In a fourth aspect, the present application provides a computer readable storage medium storing computer program instructions readable by a computer, the computer program instructions being configured to perform operations of a user behavior analysis method as any one of the above, when run.
Drawings
FIG. 1 is a flow chart of a user behavior analysis method in an embodiment;
FIG. 2 is a block diagram of a user behavior analysis system in an embodiment;
fig. 3 is a block diagram of an electronic device in an embodiment.
Detailed Description
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the present application will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present application, but is not intended to limit the present application.
Example 1:
the present embodiment discloses a user behavior analysis method, which may be performed by, but not limited to, a computer device or a virtual machine with a certain computing resource, for example, an electronic device such as a personal computer, a smart phone, a personal digital assistant, or a wearable device, or a virtual machine.
As shown in fig. 1, a method for analyzing user behavior may include, but is not limited to, the following steps:
s1, acquiring user information of a designated user; the user information comprises user basic information and user behavior information; in this embodiment, the user basic information includes personal basic condition information such as account identification information extracted from user registration information, user gender and user age, bin holding information extracted from user account transaction information, user personal credit information extracted from a third party credit platform, and the user behavior information includes browsing content, browsing times, browsing operation number, browsing time, and the like, which are not limited herein. In addition, in this embodiment, the user information of the designated user may be obtained once a week, once a month, etc., which is not limited herein, so as to realize dynamic assessment and risk early warning of the risk bearing capability of the user.
In this embodiment, the user basic information is extracted from the user registration information; user behavior information may be extracted from a target website using, but is not limited to, google Analysis (GA) tools, a data analysis tool based on user behavior developed by Google corporation. Specifically, the data analysis dimension of the GA tool is more detailed, the application process is more flexible, user operation data, target conversion data and the like of the target website can be conveniently and intuitively known, in this embodiment, the GA tool can collect user information based on Ajax, when collecting the user information, through inserting a JavaScript statistical code at one end on a page of the target website, when the user accesses the target website, the statistical code is executed, and at this time, user behavior information of the user on the target website can be obtained.
The quantity of user behavior information such as securities trade data quantity is huge, and meanwhile, a large amount of noise and errors exist in the information, so that the availability of the data is low, and the accuracy of user behavior analysis is low; meanwhile, because the data volume of the user behavior information is huge, the data processing efficiency is lower when user behavior analysis is performed based on the data volume. In order to solve the above-mentioned existing problems, in this embodiment, after obtaining the user information of the designated user, the method further includes:
A1. preprocessing the user information to obtain preprocessed user information; when the user information is preprocessed, the preprocessing such as cleaning, deduplication, missing value filling, outlier processing and the like may be performed on the user information, so as to ensure the data quality and accuracy when the user behavior analysis is performed.
A2. And carrying out noise elimination processing on the preprocessed user information to obtain denoised user information so as to obtain user deep information according to the denoised user information.
In this embodiment, after user information of a designated user is obtained, the user information after noise removal is obtained by performing preprocessing, noise elimination and other processes on the user information, and then subsequent data processing is performed according to the user information after noise removal, so that user behavior analysis is performed, the overall quality of data is improved, the accuracy of user analysis and identification and the data processing efficiency can be improved, further, security companies can be facilitated to better cope with user risks, and meanwhile, security companies can be facilitated to better formulate differentiated marketing strategies for users, so that accurate marketing is realized.
S2, obtaining deep user information according to the user information; the user deep information comprises user category depth information corresponding to the user basic information and user behavior depth information corresponding to the user behavior information.
Correspondingly, obtaining deep user information according to the user information comprises the following steps:
classifying the basic information of the user to obtain user class characteristics;
performing Hot independent coding (namely One-Hot coding, also called One-bit effective coding) on the user category characteristics, and converting the user category characteristics into user category characteristic vectors;
and performing a first convolution operation on the user category feature vector to obtain user category depth information.
Obtaining deep user information according to the user information, and further comprising:
classifying the user behavior information to obtain user behavior characteristics;
performing hot independent coding on the user behavior characteristics, and converting the user behavior characteristics into user behavior characteristic vectors;
and performing a second convolution operation on the user behavior feature vector to obtain user behavior depth information.
In this embodiment, the user information is processed by using the hot independent code, which is beneficial to improving the processing efficiency of the user information and facilitating the subsequent operations such as classifying the user based on the user information.
In addition, in this embodiment, a preset convolution network is used to perform convolution operation on the user category feature vector and the user behavior feature vector, so that implicit information of the user category feature vector and the user behavior feature vector, that is, user category depth information and user behavior depth information, can be extracted, and further accuracy of subsequent data analysis and processing can be improved conveniently.
In this embodiment, the data mining processing of the user information is implemented through convolution operation, so that as much hidden information as possible can be extracted from the user information containing a large amount of redundant information, which is convenient for fast processing of the massive user information, and further provides a data basis for subsequent user behavior analysis. It should be understood that, in this embodiment, when deep information of a user is obtained, the deep information of the user may also be obtained through a clustering algorithm, and specifically, behaviors of different users may be clustered according to similarity, so as to implement classification processing on the behaviors of the user, and further facilitate prediction on the behaviors of the user.
S3, obtaining a risk bearing capacity value of the current appointed user and a user group to which the current appointed user belongs according to the deep information of the user.
According to the deep information of the user, obtaining the risk bearing capacity value of the current appointed user and the user group to which the current appointed user belongs, wherein the method comprises the following steps:
s301, obtaining a risk bearing capacity value of a current appointed user according to deep information of the user;
s302, according to the risk bearing capacity value, extracting all matched users with the difference value between the sampling risk bearing capacity value and the risk bearing capacity value smaller than a preset value from a pre-stored sampling user group; it should be noted that, the difference between the risk bearing capacity value of the matching user and the risk bearing capacity value of the current appointed user is smaller than the preset value, that is, the risk bearing capacity level of the matching user is the same as the risk bearing capacity level of the current appointed user, and the risk bearing capacity values of the current appointed user in all the matching users can be ordered according to the asset configuration conditions of all the matching users, so that basis is provided for asset configuration early warning and the like of the current appointed user.
S303, combining all the matched users to obtain the user group of the current appointed user.
S4, sorting the risk bearing capacity values and the risk bearing capacity values of all users in the user group to obtain a sorting result.
S5, obtaining the risk bearing capacity grade of the current appointed user according to the sequencing result; it should be noted that the risk tolerance level is not limited herein, such as aggressive (e.g., the ranking result is at the first 30% of the risk tolerance values of the users in the user group), smooth (e.g., the ranking result is at 30% -70% of the risk tolerance values of the users in the user group), conservative (e.g., the ranking result is at the last 30% of the risk tolerance values of the users in the user group), and so on.
S6, obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user. In this embodiment, the user asset configuration early warning result includes an early warning level, if the risk level of the configuration scheme corresponding to the asset configuration information of the current designated user is higher than the risk bearing capacity level, the early warning level is higher so as to prompt the user to change the asset configuration scheme as soon as possible, avoid the user to bear investment risk greater than the risk bearing capacity of the user, and if the risk level of the configuration scheme corresponding to the asset configuration information of the current designated user is lower than the risk bearing capacity level, the early warning level is lower so as to prompt the user to properly adjust the asset configuration scheme of the current designated user.
In this embodiment, after obtaining the risk tolerance capability level of the current designated user, the method further includes:
s7, updating the asset configuration information of the current appointed user according to the risk bearing capacity level to obtain and output updated asset configuration information.
The embodiment can evaluate the risk of the investor, and is beneficial to realizing the risk early warning of the investor. Specifically, in the implementation process of the embodiment, the user information of the appointed user is obtained, the user deep information is obtained according to the user information, and then the risk bearing capacity value of the current appointed user and the user group to which the current appointed user belongs are obtained according to the user deep information; then, sorting the risk bearing capacity values and the risk bearing capacity values of all users in the user group to obtain a sorting result, and obtaining the risk bearing capacity grade of the current appointed user according to the sorting result; and finally, obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user. In the process, the embodiment realizes the risk assessment of the investor, is beneficial to the securities corporation to comprehensively master the risk of the investor, can be beneficial to finding out the problems and pain points encountered by the investor in the securities trading process, provides better user experience, is convenient for better knowing the investment demand and the investment preference of the investor, more accurately performs market positioning and user portrayal, is convenient for the securities corporation to provide more accurate investment service for the investor, thereby saving marketing cost, improving market competitiveness, better monitoring the trading behavior of the investor, predicting user risk and performing user risk management, and has popularization and application values.
Example 2:
the embodiment discloses a user behavior analysis system, which is used for realizing the user behavior analysis method in the embodiment 1; as shown in fig. 2, the user behavior analysis system includes:
the user information acquisition module is used for acquiring user information of a designated user;
the user deep information generation module is in communication connection with the user information acquisition module and is used for acquiring user deep information according to the user information;
the risk assessment module is in communication connection with the user deep information generation module and is used for obtaining a risk bearing capacity value of the current appointed user and a user group to which the current appointed user belongs according to the user deep information; the risk bearing capacity value is used for sorting the risk bearing capacity values and the risk bearing capacity values of all users in the user group to obtain a sorting result; the risk bearing capacity grade of the current appointed user is obtained according to the sequencing result;
and the risk early warning module is in communication connection with the risk assessment module and is used for obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user.
Example 3:
on the basis of embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like. An electronic device may be referred to as being used for a terminal, a portable terminal, a desktop terminal, etc., as shown in fig. 3, the electronic device includes:
a memory for storing computer program instructions; the method comprises the steps of,
a processor for executing computer program instructions to perform the operations of the user behavior analysis method as in any one of embodiment 1.
In particular, processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 301 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 301 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen.
Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 302 is used to store at least one instruction for execution by processor 301 to implement the user behavior analysis method provided in embodiment 1 of the present application.
In some embodiments, the terminal may further optionally include: a communication interface 303, and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected by a bus or signal lines. The respective peripheral devices may be connected to the communication interface 303 through a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, a display screen 305, and a power supply 306.
The communication interface 303 may be used to connect at least one peripheral device associated with an I/O (Input/Output) to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, either or both of the processor 301, the memory 302, and the communication interface 303 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.
The Radio Frequency circuit 304 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuitry 304 communicates with a communication network and other communication devices via electromagnetic signals.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof.
The power supply 306 is used to power the various components in the electronic device.
Example 4:
on the basis of any one of embodiments 1 to 3, this embodiment discloses a computer-readable storage medium for storing computer-readable computer program instructions configured to perform operations of the user behavior analysis method of embodiment 1 when run.
It will be apparent to those skilled in the art that the modules or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device for execution by the computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solution of the present application, and not limiting thereof; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents. Such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method for analyzing user behavior, characterized in that: comprising the following steps:
acquiring user information of a designated user;
obtaining user deep information according to the user information;
obtaining a risk bearing capacity value of a current appointed user and a user group to which the current appointed user belongs according to the user deep information;
the risk bearing capacity value and the risk bearing capacity values of all users in the affiliated user group are ranked to obtain a ranking result;
according to the sorting result, obtaining the risk bearing capacity grade of the current appointed user;
and obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user.
2. A method of user behavior analysis according to claim 1, wherein: the user information comprises user basic information and user behavior information; wherein, the user basic information is extracted from the user registration information; the user behavior information is extracted from the target website by using a Google analysis tool.
3. A method of user behavior analysis according to claim 1, wherein: after obtaining the user information of the designated user, the method further comprises:
preprocessing the user information to obtain preprocessed user information;
and carrying out noise elimination processing on the preprocessed user information to obtain denoised user information so as to obtain user deep information according to the denoised user information.
4. A method of user behavior analysis according to claim 2, wherein: the user deep information comprises user category depth information corresponding to the user basic information; correspondingly, obtaining the deep user information according to the user information comprises the following steps:
classifying the user basic information to obtain user class characteristics;
performing hot independent coding on the user category characteristics, and converting the user category characteristics into user category characteristic vectors;
and performing a first convolution operation on the user category feature vector to obtain user category depth information.
5. A method of user behavior analysis according to claim 4, wherein: the user deep information also comprises user behavior depth information corresponding to the user behavior information; correspondingly, obtaining the deep user information according to the user information, and further comprises:
classifying the user behavior information to obtain user behavior characteristics;
performing hot independent coding on the user behavior characteristics, and converting the user behavior characteristics into user behavior characteristic vectors;
and performing a second convolution operation on the user behavior feature vector to obtain user behavior depth information.
6. A method of user behavior analysis according to claim 1, wherein: obtaining a risk bearing capacity value of the current appointed user and a user group to which the current appointed user belongs according to the user deep information, wherein the method comprises the following steps:
obtaining a risk bearing capacity value of a current appointed user according to the user deep information;
according to the risk bearing capacity value, extracting all matched users with the difference value between the sampling risk bearing capacity value and the risk bearing capacity value smaller than a preset value from a pre-stored sampling user group;
and combining all the matched users to obtain the user group of the current appointed user.
7. A method of user behavior analysis according to claim 1, wherein: after obtaining the risk bearing capacity level of the current appointed user, the method further comprises the following steps:
and updating the asset configuration information of the current appointed user according to the risk bearing capacity level to obtain and output updated asset configuration information.
8. A user behavior analysis system, characterized by: for implementing the user behavior analysis method according to any one of claims 1 to 7; the user behavior analysis system includes:
the user information acquisition module is used for acquiring user information of a designated user;
the user deep information generation module is in communication connection with the user information acquisition module and is used for acquiring user deep information according to the user information;
the risk assessment module is in communication connection with the user deep information generation module and is used for obtaining a risk bearing capacity value of a current appointed user and a user group to which the current appointed user belongs according to the user deep information; the risk bearing capacity value is used for sorting the risk bearing capacity values and the risk bearing capacity values of all users in the affiliated user group to obtain a sorting result; the risk bearing capacity grade of the current appointed user is obtained according to the sorting result;
and the risk early warning module is in communication connection with the risk assessment module and is used for obtaining and outputting a user asset configuration early warning result according to the risk bearing capacity grade and the asset configuration information of the current appointed user.
9. An electronic device, characterized in that: comprising the following steps:
a memory for storing computer program instructions; the method comprises the steps of,
a processor for executing the computer program instructions to perform the operations of the user behavior analysis method of any one of claims 1 to 7.
10. A computer readable storage medium storing computer program instructions readable by a computer, characterized by: the computer program instructions are configured to perform the operations of the user behavior analysis method of any one of claims 1 to 7 when run.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311316608.0A CN117057809A (en) | 2023-10-12 | 2023-10-12 | User behavior analysis method, system, electronic equipment and medium |
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| CN202311316608.0A CN117057809A (en) | 2023-10-12 | 2023-10-12 | User behavior analysis method, system, electronic equipment and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109636084A (en) * | 2018-10-22 | 2019-04-16 | 中国平安人寿保险股份有限公司 | Consumer's risk type evaluation method, storage medium and equipment based on big data |
| CN115358878A (en) * | 2022-08-29 | 2022-11-18 | 中国银行股份有限公司 | Financing user risk preference level analysis method and device |
| CN115408621A (en) * | 2022-08-12 | 2022-11-29 | 中国测绘科学研究院 | Interest point recommendation method considering linear and nonlinear interaction of auxiliary information features |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109636084A (en) * | 2018-10-22 | 2019-04-16 | 中国平安人寿保险股份有限公司 | Consumer's risk type evaluation method, storage medium and equipment based on big data |
| CN115408621A (en) * | 2022-08-12 | 2022-11-29 | 中国测绘科学研究院 | Interest point recommendation method considering linear and nonlinear interaction of auxiliary information features |
| CN115358878A (en) * | 2022-08-29 | 2022-11-18 | 中国银行股份有限公司 | Financing user risk preference level analysis method and device |
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